import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt

# x = torch.linspace(-5, 5, 200)
# x = Variable(x)
# x_np = x.data.numpy()
#
# y_relu = F.relu(x).data.numpy()
# y_sigmoid = F.sigmoid(x).data.numpy()
# y_tanh = F.tanh(x).data.numpy()
# y_softplus = F.softplus(x).data.numpy()
#
# plt.figure(1, figsize=(8, 6))
# plt.subplot(221)
# plt.plot(x_np, y_relu, c="red", label="relu")
# plt.ylim((-1, 4))
# plt.legend(loc="best")
#
# plt.subplot(222)
# plt.plot(x_np, y_sigmoid, c="red", label="sigmoid")
# plt.ylim((-0.2, 1.2))
# plt.legend(loc="best")
#
# plt.subplot(223)
# plt.plot(x_np,y_tanh,c="red",label="tanh")
# plt.ylim((-1.2,1.2))
# plt.legend(loc="best")
#
# plt.subplot(224)
# plt.plot(x_np,y_softplus,c="red",label="softplus")
# plt.ylim((-0.2,6))
# plt.legend(loc="best")
# plt.show()


